Researchers at the University of Bath have combined genetic data with mathematical modelling to provide insights into cells and how they differentiate. The findings, to be published in open-access journal PLoS Genetics on September 1st, demonstrate the utility of a systems biology approach and could have implications for understanding and treating diseases, including cancers, caused when cells start to function incorrectly.
All cells derive from multipotent precursor cells (stem cells). The mechanisms by which stably differentiated cell-types are generated from stem cells are important to the understanding of normal development. Little is known, however, about how the switch of a stem cell to a stably differentiated state is regulated. It is likely that destabilisation of such transitions in human skin cells (melanocytes) are factors in initiating melanoma.
The research team, led by Dr. Robert Kelsh, used the model organism zebrafish to explore in vivo the gene regulatory network (GRN) that governs melanocyte differentiation. "We used our genetic data to draw an initial diagram and then applied mathematical modelling to it to assess the mathematical predictions of that diagram," explains Dr. Kelsh. "We then used existing and new experimental data to test those predictions; where necessary, we rethought our understanding of the cell and redrew the diagram. We went through this process three times, creating a more accurate picture of the cell each time." The iterative process enabled a rigorous, methodical exploration of the core melanocyte GRN, allowing the researchers to predict and subsequently validate experimentally two novel features of the GRN.
Through understanding exactly what changes take place in melanocyte development in healthy tissue and during the onset of diseases such as melanoma, scientists can work towards developing methods for potentially reversing or preventing these changes, and halting the progression of the disease.
Dr Kelsh said: "This research is an on-going collaboration between mathematical modellers and biologists. We are now looking in more detail at the core of the cell model we have come up with, and are hoping to extend the research and further develop this combined-approach technique."
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